Demand for high speed wireless communications is increasing at a fast pace. This is driven both by the sheer number of wireless communication terminals being activated as well as the increasing bandwidth demand. The latter is in turn driven by the increasing number of applications consuming the bandwidth, e.g., streaming multimedia, web browsing, GPS functionality, etc. As the computation capacity of the wireless communication terminals increases, so too do the terminals' ability to execute complex, bandwidth consuming applications.
Wireless communication networks, such as cellular networks, operate by sharing resources among the mobile terminals operating in the communication network. As part of the sharing process, resources relating to assigned channels, codes, etc. are allocated by one or more controlling devices within the system. Certain types of wireless communication networks, e.g., orthogonal frequency division multiplexed (“OFDM”) networks, are used to support cell-based high speed services such as those under certain standards such as the 3rd Generation Partnership Project (“3GPP”) e.g., Long Term Evolution (“LTE”), 3GPP2, e.g., Ultra-Mobile Broadband (“UMB”) and the IEEE 802.16 broadband wireless standards. The IEEE 802.16 standards are often referred to as WiMAX or less commonly as WirelessMAN or the Air Interface Standard.
OFDM technology uses a channelized approach and divides a wireless communication channel into many sub-channels which can be used by multiple mobile terminals at the same time. These sub-channels and hence the mobile terminals can be subject to interference from adjacent cells and other mobile terminals because neighboring base stations and mobile terminals can use the same time and frequency resource blocks. The result is that spectral efficiency is reduced, thereby reducing both communication throughput as well as the quantity of mobile terminals that can be supported in the network.
This problem is further exacerbated in multiple input, multiple output (“MIMO”) environments. Multiple Input, Multiple Output Orthogonal Frequency Division Multiplexing (“MIMO-OFDM”) is an OFDM technology that uses multiple antennas to transmit and receive radio signals. MIMO-OFDM allows service providers to deploy wireless broadband systems that take advantage of the multi-path properties of environments using base station antennas that do not necessarily have line of sight communications with the mobile terminal.
MIMO systems use multiple transmit and receive antennas to simultaneously transmit data, in small pieces to the receiver, which processes the separate data transmissions and puts them back together. This process, called spatial multiplexing, can be used to proportionally boost the data-transmission speed by a factor equal to the smaller of the number of transmitting and receiving antennas. In addition, since all data is transmitted both in the same frequency band and with separate spatial signatures, this technique utilizes spectrum very efficiently.
MIMO operation implements a channel matrix (N×M) where N is the number of transmit antennas and M is the number of receive antennas to define the coding and modulation matrix for the wireless communication channel as a whole. The less correlated each column in the matrix is, the less interference experienced in each channel (as a result of the multiple antennas). In the case where there is a totally uncorrelated arrangement, i.e., the dot product between columns is zero, the channels are considered orthogonal to one another. Orthogonality provides the least antenna-to-antenna interference, thereby maximizing channel capacity, and data rate due to the higher post-processing signal to interference and noise ratio (“PP-SINR”). PP-SINR is the SINR after the MIMO decoding stage.
Virtual MIMO (“V-MIMO”), also referred to as Multi-User MIMO (“MU-MIMO”) implements the MIMO technique described above by using multiple simultaneously transmitting mobile terminals each having one or more antennas. The serving base station includes multiple antennas. Although the base station can treat virtual MIMO operation as traditional MIMO in which a single mobile terminal has multiple antennas and can separate and decode the transmissions from the multiple simultaneously transmitting mobile terminals, channel correlation among mobile terminals as discussed above results in channel capacity loss due to inter-mobile terminal interference.
Because wireless communication channels are subject to interference and distortion, techniques have been developed to estimate certain properties of the channel so that the receiver, e.g., base station, can take these properties into account when decoding the received data. For example, multipath distortion and fading can alter the amplitude and phase of the transmitted wireless signal. The result is that, if the wireless communication channel is not accurately estimated, the decoded data will likely be improperly decoded. For example, a 16QAM or 64QAM (quadrature amplitude modulation) signal modulates a plurality of bits. Decoding of those bits is based on the amplitude and phase of the received signal as applied to a modulation constellation. If the amplitude and/or phase of the transmitted signal changes by the time it is received at the receiver, the mapping to the constellation will be errant, resulting in improper decoding. If the channel can be estimated by the receiver, the changes in amplitude and phase can be considered by the receiver during the mapping and decoding process.
The problem is made even more complex in V-MIMO environments. V-MIMO relies on spatial multiplexing. In order to properly recover the signal, the receiver also must decorrelate the signals and remove interference. These tasks have traditionally been done in the time domain. These tasks are quite processing and time intensive when 2, 4 or more mobile terminals are part of the V-MIMO arrangement. The result is that the cost of equipment at the receiver becomes exorbitant, if it even can be implemented all.
Also, while techniques for channel estimation based on least squares algorithms are known, these techniques are insufficient for V-MIMO implementations, such as where two or more mobile terminal signals are superimposed in a set of resource blocks. Even the use of known minimum mean square error (“MMSE”) techniques fall short for V-MIMO applications.
Therefore what is needed is a cost effective, scalable and processing efficient system and method for estimating a wireless communication channel and cancelling interference that can be used in a V-MIMO environment such as on the base station uplink receiver in an LTE network.